The document analyzes air quality data from the Great Gulf Wilderness collected between 1995-2012. It finds some linear relationships between parameters like fine sulfur and sulfate concentrations, and between PM2.5, sulfur, and hydrogen concentrations. However, it finds little correlation between the major components of haze like sulfate, nitrate, organic carbon, elemental carbon, and soil. Trendlines of the data for each component show very small decreasing slopes, indicating concentrations may slightly decrease over time. Overall correlation between sulfate and nitrate concentrations is also very low.

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Noah Bernays
Staff Scientist Georgia Murray
7/12/13
Methodology and Analysis of Great Gulf Wilderness Air Quality Data
Cleaning up the data:
I started by downloading all the missing IMPROVE data for the Great Gulf Wilderness from
http://views.cira.colostate.edu/fed/DataWizard/Default.aspx. These included both non-summer
data from 1995-2008 and all data from 2009-2012. Note that we are missing data from January-
May of 2000 (they do not appear on the website)- unlike other missing values where there is
simply a dot, the dates in that time range just do not show up. Also note that the website does not
have any data points for NH4f: Value. Next I inserted a dot wherever it said “-999”, “0”, N/A”,
or “#N/A.” I added averages, medians, and modes for the data points from 1995-2012 for each
individual parameter (except status flags). I then made a chart of the ratios of the average of each
parameter to that of each other parameter. Note that although some ratios are certain values
divided by themselves, some of them come out close to 1 but not exactly 1, probably due to a
difference in decimal places at some point. I cleaned up the page of metadata by putting the
information into a chart (the columns make it easier to read and interpret). Using the raw data, I
made a scatter plot for each individual parameter of all the data points over time and one scatter
plot of all parameters’ data points over time. Excel interprets the dots as “0”s, so the trendlines
for those scatter plots would be skewed by a wrong interpretation of the missing data. So I took
the raw data and took only the dates for which values existed, and plotted those on a scatter plot.
I added trendlines to those graphs. I then included the n-value for each graph without missing
values to determine if we would be able to make any valid or valuable conclusions from the data.

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Next I made a chart of the trendlines for the individual parameters and graphed them all together
(x-values=0,1,2,3, etc). I wanted to see if there were any conclusions I could make about two or
more parameters possibly being correlated to each other. Note that each parameter’s trendline
has a negative slope, except for HF: UNC (0.00000001), HF: MDL (0.0000006), HF: FR
(0.0002), MF: FR (0.0001), and SO4f: FR (0.0002). Since the trendlines had very different y-
intercepts (ranging from about -0.02 to about 34.4) it was hard to determine anything about
similar slopes. I therefore started to make 4 different graphs, one of the trendlines with y-
intercepts ranging from -1 to 5, one with y-intercepts 5.5 to 7, another from 11 to 19, and a forth
with y-intercepts greater than 23. Next I started making a chart of the 75th
and 25th
percentiles for
each parameter’s data set, including those horizontal lines (i.e. y=”75th
percentile) in the graphs
of the parameters without missing values.
Analyzing the data:
• Insert the data into the Aerosol_Calculations document.
• Describe the relationships between Sf, SO4f, Hf, MFf, and SOILf (use both the
individual trendlines and the graphs of the parameters against each other).
• Describe the make-up of haze with reference to the relationships described above.
• How will haze be affected given the trendlines of each of the individual parameters that
make up haze?
Bullets of discrete time periods and concentrations spikes/trends due to certain
events/legislation/building of power plants, etc.
Yearly medians (graphs), 75th
, 50th
, and 25th
percentiles
The data with the most linear relationships are found in Figures 175, 173, and 174 below.
Note: all units are in µg/m3

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Figure 175 shows a linear relationship between fine sulfur and fine sulfate concentrations. The
ratio of the two is approximately 1, indicating that as the concentration of sulfate increases by
1µg/m3
, so does the concentration of sulfur. Normally, the ratio of the concentrations of
sulfate:sulfur would be 3:1 because the atomic mass of sulfate is three times that of sulfur.
However, we are using the “corrected values” of each parameter, meaning that only the values
that were obtained with a flow rate between 20.9 and 23.9 L/min were kept. The sulfur
concentration values were then multiplied by 3 in order to create a 1:1 ratio.
Figure 173 shows linear relationships between PM2.5 and fine hydgrogen (red squares), and
between sulfur and fine hydrogen (blue diamonds). The trendline for the PM2.5 series has a slope
of approximately 18.5, meaning that as the concentration of hydrogen increases by 1µg/m3
, the
concentration of PM2.5 increases by about 18.5 µg/m3
. The trendline for the sulfur series has a
slope of almost 8, indicating that as the concentration of hydgrogen increases by 1µg/m3
, the
concentration of sulfur increases by almost 8µg/m3
.

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Figure 174 shows a linear relationship between sulfate and sulfur and PM2.5- because sulfur and
sulfate are linear with a slope of about 1, both trendlines have the same slope (approximately
0.43) when plotted against PM2.5. As the concentration of PM2.5 increases by 1µg/m3
, the
concentrations of both sulfur and sulfate increase by about 0.43µg/m3
.
Further evaluation of the major components of haze indicates much less correlation of the data.
Haze:
The major components of haze are sulfate aerosol, nitrate aerosol, organic carbon, elemental,
carbon, and crustal (taken from “hazehutstalk” Powerpoint). The following graphs present the
levels of each of these parameters’ concentrations on every 3rd
day from 6/10/1995 to 6/29/2012.
Note: the x-values (dates) are consolidated, but all the values are present

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The data in Figure 46 are very spread out, and the correlation coefficient is only 0.0022 (where a
value of 1 indicates a linear relationship), so there is little evidence to support a trend. Simply
creating a trendline of the data yields a slope of approximately –0.0001. In other words, if the
values were to continue at the current trend, concentrations of sulfate would decrease by
0.0001µg/m3
every 3rd
day.
In Figure 4, the data are similarly diffuse, with an R2
value of 0.0634, indicating that the
relationship between the values and the dates do not appear to be related.. However, a trendline
of the data has a slope of -0.00004, meaning that concentrations of nitrate would decrease by
0.00004µg/m3
every 3rd
day if it continued at its current levels.

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The R2
value of the data in Figure 12 is 0.0459, a number too small to infer direct correlation.
Similar to sulfate, a trendline of the data has a slope of -0.0001, meaning that concentrations of
organic carbon would decrease by 0.0001µg/m3
every 3rd
day if it continued as is.
The R2
value of the data in Figure 8 is 0.1088, a number too small to infer direct correlation. A
trendline of the data has a slope of -0.00003, meaning that concentrations of elemental carbon
would decrease by 0.00003µg/m3
every 3rd
day if it continued as is.

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The R2
value of the data in Figure 61 is 0.0164, a number too small to infer direct correlation. A
trendline of the data has a slope of -0.00002, meaning that concentrations of fine soil would
decrease by 0.00002µg/m3
every 3rd
day if it continued as is.
Correlation analysis:
The correlation of the data in Figure 98 is very low, with an R2
value of about 0.06 (a value of 1
indicates a linear relationship). We therefore cannot determine a direct correlation between the
concentrations of sulfate and nitrate.

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The correlation of the data in Figure 98 is very low, with an R2
value of about 0.06 (a value of 1
indicates a linear relationship). We therefore cannot determine a direct correlation between the
concentrations of sulfate and nitrate.

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